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Features of Apache Spark
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in this video we are discussing features
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of Apache spark and there are eight
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different features we are going to
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introduce to you so features of Apache
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spark were having speed reusability
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advanced analytics were having in memory
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computing real-time stream processing
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we're having lazy volition dynamic in
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nature and also fault tolerance so these
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are the eight different features spark
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is having so let us discuss one by one
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at was we are starting with the speed we
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know that this spark is actually
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lightning fast cluster computing
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technology and it supports in-memory
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competition so that's why it's
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competition is hundred times faster than
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the other technologies available so
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Apache spark has a high data processing
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speed by reducing the number of
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read/write operations on disks it can be
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hundred times faster in memory so
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because there were minimized the
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read/write operations on the disk so the
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competitions are getting done in memory
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so that's why it is hundred times faster
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next one is the reusability spark codes
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can be easily converted to batch
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processing or join stream on historical
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data and that's why it is having the
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high reusability next one is the
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advanced analysis advanced analysis
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means there are different tools which
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are dedicated to do specific walks in
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Apache spark so in our in our Apache
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spark components we have shown you that
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in the ecosystem there are six different
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components are there and those
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components were those tools are
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dedicated for different kinds of
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analysis oriented activities so there
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are different tools which are dedicated
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to do specific works in Apache spark and
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some of them are machine learning
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library also known as ml Lib we're
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having the graphics processing tool also
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known as graph X and etc next one is
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in-memory computation very important
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feature at the SPARC works
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on data which are stored in the memory
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we know that memory accessing time will
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be quite less compared to the disk
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accessing time so as the competition is
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getting done in to the memory that means
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that data will be available in the
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memory and during processing when the
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intimated data will be produced
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intermediate results will be produced
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and they'll be also kept in the memory
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so at the spark walks on data and which
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are stored in the memory so it helps to
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increase the processing speed next one
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is the real-time processing we can do
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real-time processing in SPARC it is one
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of the advantages of spark over Hadoop
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and it can process only the present data
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so that means real-time processing means
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depending upon the present input the
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processing will be done the analysis
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will be done and outputs will be
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obtained next one is the Lizzie
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evaluation the SPARC transformation does
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not change the data on the go and only
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when one action is triggered then only
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that changes will be done on the actual
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data so it is not it is not getting done
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before initiation or before triggering
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of any action when the action will be
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triggered then only that respective
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changes will be done on the actual data
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next one is the dynamic in nature
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so there are 80 high-level operations
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are present in SPARC so eight T
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high-level operations are present in
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SPARC so we can build parallel
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applications in it so applications will
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be working in parallel and simultaneous
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next one is about fault tolerance using
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sparks code abstraction rdd's we can
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handle different errors we know that in
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case of a DD it is nothing but one data
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structure where one data set will be
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divided and will be distributed on to
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multiple different servers as a result
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of that the probability of data loss has
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got reduced and that's why the system is
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known as fault tolerance so it is it is
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it reduces the data loss in our spark
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system so these are the different eight
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features of spie patches for which we
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have discussed in this video thanks for
4:23
watching this video
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