1 / 2100%
“Sparkh ish ah generalh distributedh datah processingh engineh builth forh
speed,h easeh ofh use,h andh flexibility”,h “Ith hash becomeh oneh ofh theh criticalh
componentsh inh theh bigh datah stackh dueh toh itsh easeh ofh use,h speed,h andh
flexibility.”,h Companies,h inh differenth industries,h haveh widelyh adoptedh
thish scalableh datah processingh system.h h (Luu.h 2021).h Inh theh aboveh
referencesh fromh theh book,h theh firsth lineh ofh quoteh referredh toh speed,h
easeh ofh use,h andh flexibility.h Also,h theh secondh lineh ofh theh quoteh
mentionedh speed,h easeh ofh use,h andh flexibility.h Andh theh thirdh lineh ofh
quoteh toldh ush thath companiesh adopth sparkh becauseh ofh itsh scalableh
datah processingh system.h Sparkh hash severalh advantagesh whenh
comparedh toh otherh bigh datah andh MapReduceh technologiesh likeh
Hadooph andh Storm.h Inh performance,h sparkh runsh 100h timesh fasterh inh
memory,h andh 10h timesh fasterh onh disk,h thanh Hadoop.h Inh datah
processing,h Sparksh performsh bothh batch,h real-time,h andh graphh
processing,h buth Hadooph processesh datah inh batchesh only.h Hadoop’sh
MapReduceh ish complex,h whileh Sparksh supportsh user-friendlyh APIs.h
Apacheh Sparkh hash itsh ownh scheduler,h whileh Hadooph dependsh onh anh
externalh scheduler.h Therefore,h basedh onh theh above-statedh
advantages,h toolsh suchh ash Sparkh areh requiredh forh bigh datah computingh
byh companies.
Ah clusterh computingh ish ah seth ofh connectedh computersh (nodes)h thath
workh togetherh ash ifh theyh areh ah singleh (muchh moreh powerful)h machine,h
toh ensureh highh availabilityh andh highh performance.h Tasksh areh assignedh
toh nodes,h andh eachh nodeh runsh itsh ownh instanceh ofh anh operatingh
system.h Theh systemh willh noth beh interruptedh ifh ah singleh nodeh fails,h theh
otherh nodesh willh continueh toh provideh uninterruptedh processing.h
Clusterh computingh willh impacth businessesh ofh allh sizes,h byh reducingh
costh andh preventingh bottlenecks,h thereby,h improvingh performance.h
Also,h businessesh canh centralizeh managedh softwareh thath makesh theh
nodesh available.
Asideh fromh Apacheh Spark,h thereh areh otherh toolsh (options)h availableh
forh computingh largeh volumesh ofh unstructuredh andh structuredh datah
withh lowh latency.h Theh Apacheh Hadooph ish open-sourceh softwareh forh
bigh datah frameworks,h whichh allowsh distributedh processingh ofh largeh
datah setsh acrossh clustersh ofh computers.h Oneh majorh advantageh ofh
Hadooph ish itsh highh toleranceh features.h Apacheh Stormh ish anotherh
open-sourceh computationh tool,h whichh offersh distributedh real-time,h
fault-toleranth processingh system.h Thereh areh otherh toolsh thath areh
fastest,h easiest,h andh highlyh secureh modernh forh bigh datah platforms,h
Cloudera,h Qubole,h Cassandra,h CouchDB,h Atlas.ti,h andh soh on.
Amongh theseh toolsh forh bigh datah platforms,h Ih willh agreeh withh Taylorh
thath Hadoop,h Atlas.ti,h HPCC,h Storm,h Qubole,h Cassandra,h Statwing,h
andh CouchDBh areh someh ofh theh Besth Bigh Datah toolsh (2022).h Inh myh
opinion,h Ih willh includeh Sparkh inh theh listh above,h becauseh ofh itsh speed,h
easeh ofh use,h andh flexibility.h Otherh factorsh anyoneh wouldh considerh
beforeh selectingh ah Bigh Datah toolh areh Licenseh Costh ifh applicable,h
Qualityh ofh Customerh support,h andh theh costh involvedh inh trainingh
employeesh onh theh tool.
Reference:
Luu,h H,h (Octoberh 2021).h
Beginningh Apacheh Sparkh 3:h Withh
DataFrame,h Sparkh SQL,h Structuredh Streaming,h andh Sparkh Machineh
Learningh Libraryh 2ndh Edition.h
https://learning.oreilly.com/library/view/beginning-apache-
spark/9781484273838/h
Taylor,h D.,h (2022,h Januaryh 18).h
Guru99:h Toph 15h Bigh Datah Toolsh andh
Softwareh (Openh Source)h 2022.
h https://www.guru99.com/big-data-
tools.html
h
Students also viewed