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Why Our World Would End If Apache Spark Disappeared

Apache Spark

In the current Data Analytics market, there is a lot of buzz going

around Apache Spark. Most of the business experts are labelling Spark on

top of Hadoop. If you are in to the Big Data Analytics business or ambitious of

entering the market in the coming days, then you should probably know – to

what extent does Spark rules over Hadoop? This article endeavours to help

you in locating answers to some of your latent questions. Before shedding

key focus on Spark vs Hadoop issues, let us initially discuss what Spark and

Hadoop are.

Apache Spark and Hadoop, both are the Big Data frameworks, that offersdifferent tools to performs Big Data related tasks, but not accurately the sametasks.

Originally developed in UC Berkeley’s AMPLab, and later distributed as anopen-source Project, Apache Spark is a powerful processing engine for BigData. It is a framework for performing data analytics, which provides fasterand more general data processing platform.

Apache Hadoop –

On the other hand, Hadoop is a distributed data infrastructure, whichdistributes huge data collections across several nodes within the cluster ofcommodity servers.

It further keeps a record of that data, enabling big data processing andanalytics more effective. Hadoop is largely considered as the general-purposeframework that supports multiple models.

Hadoop, for many years was traditionally used to run the Map/Reduce jobs,which usually are the long running jobs. To accelerate the process, Spark hasbeen designed to run on top of Hadoop cluster for real-time stream dataprocessing and fast interactive queries that can be completed in a fraction ofseconds.

Today, most of the projects undertake distributed storage i.e. instead ofstoring the data in a single location, it has become feasible for the businessesto store data on multiple storage devices (like disks).

For processing such distributed data spread across multiple devices, Hadoopwith its Distributed File System (HDFS) feature is defining the most scalablemeans available in the open-source community. Spark does not contain itsown system for data processing, and requires some third-party provider.This is the core reason for most of the Big Data projects for installing Sparkon top of the Hadoop.

It means, Hadoop extends its core support to both the traditionalMap/Reduce and Apache Spark, and it will be precise to consider Spark asan enhancement to Hadoop MapReduce rather than as the replacement toHadoop.

Let us shed some light on the key features of Apache Spark, which arehighlighting it in the world of ‘Big Data’.

1. Speed

Spark uses the concept of RDD (Resilient Distributed Dataset), which enablesit to store data on memory, and thereby reducing the number of read/writesto disc; data will be persisted on the disc only when it is largely required. Thismakes the applications in Hadoop clusters to run up to 100x faster inmemory, and 10x faster when running on disk.

2. Ease of Use

Spark enables to write applications in Scala, Python, or Java etc., whichmakes it highly transparent for the developers to develop and run theapplications in any of their known programming languages. Spark alsocontains a set of more than 80 in-built operators, which can be used to querythe data within the shell.

3. Effective Integration

Spark runs independent, and can also run on Hadoop, Mesos, Cassandra,Standalone or on Cloud. Its efficiency to access (read) data from any of theHadoop data sources like HDFS, HBase etc. makes it highly suitable formigrating the existing Hadoop applications.

4. Real-time Stream Processing

MapReduce primarily handles and processes the stored data; however,Spark handles the real-time streaming data i.e. it does not ignore the otherexisting frameworks that can be integrated to handle streaming in Hadoop.

5. Expanding Community

A wide set of developers from more than 50 companies have built ApacheSpark. The project was initiated in the year 2009, and today, more than 250developers have already made their valuable contributions to this boomingproject.

Conclusion:Apache Spark is the new and shiny player in the field of Big Data, whereas,Hadoop is a much more experienced player. It is the concepts like speed,performance, and ease of use that gives Spark an edge over the Hadoop.Though Spark stands as a big winner, it is with the concepts of HadoopDistributed File System that lets you to win the game by using the full BigData package.

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